Atrial Fibrillation (AF) is a common type of heart disease that leads to stroke, heart failure or other complications. Millions of people get affected by AF every year and the prevalence of the disease is likely to increase. Noninvasive detection of AF is a popular area of research for quite a long time. Irregularities in heart beat is considered to be the most common symptom of AF and can be traced in an ECG. However, being an episodic event an accurate detection of AF is not always trivial. Conventional AF detectors have been mostly of atrial activity analysis based or ventricular response analysis-based methods. The absence of P waves or the presence of f waves in the TQ interval are searched in atrial activity analysis-based AF detectors. On the other hand, time, frequency and morphological features are extracted from RR intervals to identity the heart beat irregularity in ventricular response analysis based methods. However, the traditional methods have certain limitations regarding real time deployments, firstly, most of them are validated on clinically accepted multiples of lead ECG signals, recorded for a relatively longer duration. Secondly, algorithms are mostly applied on carefully selected clean data. However, in practical scenario, ECG signals are often noisy in nature. Thirdly, size of the test dataset may often not be adequate for making a conclusion (or decision) thereby result in misclassification. Lastly, most traditional or conventional methods perform binary classification between AF and normal recordings only. However, there are many non-AF abnormal rhythms (e.g., tachycardia, bradycardia, arrhythmia etc.,) which exhibit heart beat pattern similar to AF, however these non-AF abnormal rhythms are not considered for classification. Even if considered them in the dataset makes the classification task more challenging.